Machines that are currently available for simultaneous scanning and counting of documents such as paper currency are relatively complex and costly, and relatively large in size. The complexity of such machines can also lead to excessive service and maintenance requirements. These drawbacks have inhibited more widespread use of such machines, particularly in banks and other financial institutions where space is limited in areas where the machines are most needed, such as teller areas. The above drawbacks are particularly difficult to overcome in machines which offer much-needed features such as the ability to scan bills regardless of their orientation relative to the machine or to each other, and the ability to authenticate genuineness and/or denomination of the bills.
A variety of techniques and apparatus have been used to satisfy the requirements of automated currency handling systems. At the lower end of sophistication in this area of technology are systems capable of handling only a specific type of currency, such as a specific dollar denomination, while rejecting all other currency types. At the upper end are complex systems which are capable of identifying and discriminating among and automatically counting multiple currency denominations.
Currency discrimination systems typically employ either magnetic sensing or optical sensing for discriminating among different currency denominations. Magnetic sensing is based on detecting the presence or absence of magnetic ink in portions of the printed indicia on the currency by using magnetic sensors, usually ferrite core-based sensors, and using the detected magnetic signals; after undergoing analog or digital processing, as the basis for currency discrimination. A variety of currency characteristics can be measured using magnetic sensing. These include detection of patterns of changes in magnetic flux, patterns of vertical grid lines in the portrait area of bills, the presence of a security thread, total amount of magnetizable material of a bill, patterns from sensing the strength of magnetic fields along a bill, and other patterns and counts from scanning different portions of the bill such as the area in which the denomination is written out.
The more commonly used optical sensing techniques, on the other hand, are based on detecting and analyzing variations in light reflectance or transmissivity characteristics occurring when a currency bill is illuminated and scanned by a strip of focused light. The subsequent currency discrimination is based on the comparison of sensed optical characteristics with prestored parameters for different currency denominations, while accounting for adequate tolerances reflecting differences among individual bills of a given denomination. A variety of currency characteristics can be measured using optical sensing. These include detection of a bill's density, color, length and thickness, the presence of a security thread and holes, and other patterns of reflectance and transmission. Color detection techniques may employ color filters, colored lamps, and/or dichroic beamsplitters.
In addition to magnetic and optical sensing, other techniques of detecting characteristic information of currency include electrical conductivity sensing, capacitive sensing (such as for watermarks, security threads, thickness, and various dielectric properties) and mechanical sensing (such as for size, limpness, and thickness).
A major obstacle in implementing automated currency discrimination systems is obtaining an optimum compromise between the criteria used to adequately define the characteristic pattern for a particular currency denomination, the time required to analyze test data and compare it to predefined parameters in order to identify the currency bill under scrutiny, and the rate at which successive currency bills may be mechanically fed through and scanned. Even with the use of microprocessors for processing the test data resulting from the scanning of a bill, a finite amount of time is required for acquiring samples and for the process of comparing the test data to stored parameters to identify the denomination of the bill.
Some of the currency scanning systems today scan for two or more characteristics of bills to discriminate among various denominations or to authenticate their genuineness. However, these systems do not efficiently utilize the information which is obtained. Rather, these systems generally conduct comparison based on the two or more characteristics independently of each other. As a result, the time required to make these comparisons is increased which in turn can reduce the operating speed of the entire scanning system.
Recent currency discriminating systems rely on comparisons between a scanned pattern obtained from a subject bill and sets of stored master patterns for the various denominations among which the system is designed to discriminate. As a result, the master patterns which are stored play an important role in a discrimination system's ability to discriminate among bills of various denominations as well as between genuine bills and counterfeit bills. These master patterns have been generated by scanning bills of various denominations known to be genuine and storing the resulting patterns. However, a pattern generated by scanning a genuine bill of a given denomination can vary depending upon a number of factors such as the condition of the bill, e.g., whether it is a crisp bill in new condition or a worn, flimsy bill, as well as year in which the bill was printed, e.g., before or after security threads were incorporated into bills of some denominations. Likewise, it has been found that bills which have experienced a high degree of usage may shrink, resulting in a reduction of the dimensions of such bills. Such shrinkage may likewise result in variations in scanning patterns. As a result, if, for example, a $20 master pattern is generated by scanning a crisp, genuine $20 bill, the discrimination system may reject an unacceptable number of genuine but worn $20 bills. Likewise, if a $20 master pattern is generated using a very worn, genuine $20 bill, the discrimination system may reject an unacceptable number of genuine but crisp $20 bills.
It has been found that scanning U.S. bills of different denominations along a central portion thereof provides scanning patterns sufficiently divergent to enable accurate discrimination between different denominations. Such a discrimination device is disclosed in U.S. Pat. No. 5,295,196. However, currencies of other countries can differ from U.S. currency and from each other in a number of ways. For example, while all denominations of U.S. currencies are the same size, in many other countries currencies vary in size by denomination. Furthermore, there is a wide variety of bill sizes among different countries. In addition to size, the color of currency can vary by country and by denomination. Likewise, many other characteristics may vary between bills from different countries and of different denominations.
As a result of the wide variety of currencies used throughout the world, a discrimination system designed to handle bills of one country generally can not handle bills from another country. Likewise, the method of discriminating bills of different denominations of one country may not be appropriate for use in discriminating bills of different denominations of another country. For example, scanning for a given characteristic pattern along a certain portion of bills of one country, such as optical reflectance about the central portion of U.S. bills, may not provide optimal discrimination properties for bills of another country, such as German marks.
Furthermore, there is a distinct need for an identification system which is capable of accepting bills of a number of currency systems, that is, a system capable of accepting a number of bill-types. For example, a bank in Europe may need to process on a regular basis French, British, German, Dutch, etc. currency, each having a number of different denomination values.
Some of the optical scanning systems available today employ two optical scanheads disposed on opposite sides of a bill transport path. One of the optical scanheads scans one surface (e.g., green surface) of a currency bill to obtain a first set of reflectance data samples, while the other optical scanhead scans the opposite surface (e.g., black surface) of the currency bill to obtain a second set of reflectance data samples. These two sets of data samples are then processed and compared to stored characteristic patterns corresponding to the green surfaces of currency bills of different denominations. If degree of correlation between either set of data samples and any of the stored characteristic patterns is greater than a predetermined threshold, then the denomination of the bill is positively identified.
A drawback of the foregoing technique for scanning both surfaces of a currency bill is that it is time-consuming to process and compare both sets of data samples for the scanned bill to the stored characteristic patterns. The set of data samples corresponding to the black surface of the scanned bill are processed and compared to the stored characteristic patterns even though no match should be found. As previously stated, the stored characteristic patterns correspond to the green surfaces of currency bills of different denominations.
Another drawback of the foregoing scanning technique is that the set of data samples corresponding to the black surface of the scanned bill occasionally leads to false positive identification of a scanned bill. The reason for this false positive identification is that if a scanned bill is slightly shifted in the lateral direction relative to the bill transport path, the set of data samples corresponding to the black surface of the scanned bill may sufficiently correlate with one of the stored characteristic patterns to cause a false positive identification of the bill. The degree of correlation between the set of “black” data samples and the stored “green” characteristic patterns should, of course, not be greater than the predetermined threshold for positively identifying the denomination of the bill.
Furthermore, in currency discriminating systems that rely on comparisons between a scanned pattern obtained from a subject bill and sets of stored master patterns, the ability of a system to accurately line up the scanned patterns to the master patterns to which they are being compared is important to the ability of a discrimination system to discriminate among bills of various denominations as well as between genuine bills and counterfeit bills without rejecting an unacceptable number of genuine bills. However, the ability of a system to line up scanned and master patterns is often hampered by the improper initiation of the scanning process which results in the generation of scanned patterns. If the generation of scanned patterns is initiated too early or too late, the resulting pattern will not correlate well with the master pattern associated with the identity of the currency; and as a result, a genuine bill may be rejected. There are a number of reasons why a discrimination system may initiate the generation of a scanned pattern too early or too late, for example, stray marks on a bill, the bleeding through of printed indicia from one bill in a stack onto an adjacent bill, the misdetection of the beginning of the area of the printed indicia which is desired to be scanned, and the reliance on the detection of the edge of a bill as the trigger for the scanning process coupled with the variance, from bill to bill, of the location of printed indicia relative to the edge of a bill. Therefore, there is a need to overcome the problems associated with correlating scanned and master patterns.
In some currency discriminators bills are transported, one at a time, passed a discriminating unit. As the bills pass the discriminating unit, the denomination of each bill is determined and a running total of each particular currency denomination and/or of the total value of the bills that are processed is maintained. A number of discriminating techniques may be employed by the discriminating unit including optical or magnetic scanning of bills. A plurality of output bins are provided and the discriminator includes means for sorting bills into the plurality of bins. For example, a discriminator may be designed to recognize a number of different denominations of U.S. bills and comprise an equal number of output bins, one associated with each denomination. These discriminators also include a reject bin for receiving all bills which cannot be identified by the discriminating unit. These bills may later be examined by an operator and then either re-fed through the discriminator or set aside as unacceptable.
Depending on the design of a discriminator, bills may be transported and scanned either along their long dimension or their narrow dimension. For a discriminator that transport bills in their narrow dimension, it is possible that a given bill may be oriented either face up or face down and either top edge first (“forward” direction) or top edge last (“reverse” direction). For discriminators that transport bills in their long dimension, it is possible that a given bill may be oriented either face up or face down and either left edge first (“forward” direction) or left edge last (“reverse” direction). The manner in which a bill must be oriented as it passes a discriminating unit depends on the characteristics of the discriminator. Some discriminators are capable of identifying the denomination of a bill only if it is fed with a precise orientation, e.g., face up and top edge first. Other discriminators are capable of identifying bills provided they are “faced” (i.e., fed with a predetermined face orientation, that is all face up or all face down). For example, such a discriminator may be able to identify a bill fed face up regardless of whether the top edge is fed first or last. Other discriminators are capable of identifying the denomination fed with any orientation. However, whether a given discriminator can discriminate between bills fed with different orientations depends on the discriminating method used. For example, a discriminator that discriminates bills based on patterns of transmitted light may be able to identify the denomination of a forward fed bill regardless of whether the bill is fed face up or face down, but the same discriminator would not be able to discriminate between a bill fed face up and a bill fed face down.
Currently, discriminators are known which discriminate and/or sort by denomination when bills are properly faced. In such systems, all reverse-faced bills are not identified and are routed to a reject receptacle. Also discriminators are known which discriminate and/or sort between all bills facing up and all bills facing down. For example, in a multi-output pocket system, all face up bills, regardless of denomination, may be routed to a first pocket and all face down bills, regardless of denomination, may be routed to a second pocket. Furthermore, there is currently known discriminators designed to accept a stack of faced bills and flag the detection of a reverse-faced bill, thus allowing the reverse-faced bill to be removed from the stack. However, there remains a need for a discriminator that can detect and flag the presence of a bill oriented with an incorrect forward/reverse orientation and a discriminator that can sort between forward-oriented bills and reverse-oriented bills.
Furthermore, for a number of reasons, a discriminating unit may be unable to determine the denomination of a bill. These reasons include a bill being excessively soiled, worn, or faded, a bill being torn or folded, a bill being oriented in a manner that the discriminating unit cannot handle, and the discriminating unit having poor discriminating performance. Furthermore, the discriminating unit and/or a separate authenticating unit may determine that a bill is not genuine. In current discriminators, such unidentified or non-genuine bills are deposited in a reject receptacle.
A characteristic of the above described discriminators is that the value of any rejected unidentified bills is not added to the running total of the aggregate value of the stack of bills nor do the counters keeping track of the number of each currency denomination reflect the rejected unidentified bills. While this is desirable with respect to bills which are positively identified as being fake, it may be undesirable with respect to bills which were not identified for other reasons even though they are genuine bills. While the bills in a reject receptacle may be re-fed through the discriminator, the operator must then add the totals from the first batch and the second batch together. Such a procedure can be inefficient in some situations. Also, if a bill was rejected the first time because it was, for example, excessively soiled or too worn, then it is likely that the bill will remain unidentified by the discriminating unit even if re-fed.
A problem with the above described situations where the totals and/or counts do not reflect all the genuine bills in a stack is that an operator must then count all the unidentified genuine bills by hand and add such bills to separately generated totals. As a result the chance for human error increases and operating efficiency decreases. Take for example a bank setting where a customer hands a teller a stack of currency to be deposited. The teller places the stack of bills in a discriminator, the display on the discriminator indicates that a total of $730 has been identified. However, fourteen genuine bills remain unidentified. As a result, the teller must count these fourteen bills by hand or re-fed through the discriminator and then add their total to the $730 total. An error could result from the teller miscounting the unidentified bills, the teller forgetting to add the two totals together, or the teller overlooking the unidentified bills entirely and only recording a deposit of $730. Moreover, even if the teller makes no mistakes, the efficiency of the teller is reduced by having to manually calculate additional totals. The decrease in efficiency is further aggravated where detailed records must be maintained about the specific number of each denomination processed during each transaction.
Therefore, there is a need for a currency discriminator which is capable of conveniently and efficiently accommodating genuine bills that, for whatever reason, remain unidentified after passing through the discriminating unit of a discriminator.
A number of methods have been developed for authenticating the genuineness of security documents. These methods include sensing magnetic, optical, conductive, and other characteristics of documents under test. In general, it has been found that no single authentication test is capable of detecting all types of counterfeit documents while at the same time not rejecting any genuine documents. Therefore, more than one test may be employed whereby a first test is used to detect certain types of counterfeits and additional tests are used to detect other types of counterfeits.
It has been known that the illumination of certain substances with ultraviolet light causes the substances to fluoresce, that is, to emit visible light. Some documents employ fluorescent materials as a security feature to inhibit counterfeiting. Typically, these fluorescent security features comprise a marking which is visibly revealed when the document is illuminated with ultraviolet light. Previous methods have been developed to authenticate such documents by sensing the fluorescent light emitted by a document illuminated by ultraviolet light and comparing the sensed fluorescent light to the fluorescent light emitted by genuine documents.
Conversely, some documents, such as United States currency, are manufactured from special paper designed not to fluoresce under ultraviolet light. Previously known authenticating methods for such documents have sensed for the emission of fluorescent light under ultraviolet illumination and have rejected as counterfeit those documents emitting fluorescent light.
However, it has been found that the presently known ultraviolet authentication methods do not detect all types of counterfeits. For example, while many counterfeit United States bills do emit fluorescent light under ultraviolet illumination, some counterfeit United States bills do not.